变压器
计算机科学
振动
人工智能
特征提取
深度学习
噪声测量
模式识别(心理学)
电子工程
语音识别
工程类
降噪
声学
电压
电气工程
物理
作者
Zhikai Xing,Yigang He,Xiao Wang,Jianfei Chen,Bolun Du,Liulu He,Xiaoyu Liu
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-02-23
卷期号:19 (11): 11239-11251
被引量:3
标识
DOI:10.1109/tii.2023.3245193
摘要
Machine learning methods are effective for the diagnosis of power transformer faults. However, influenced by uncertainty and noise in data, machine-learning-based diagnostic methods are still in the initial phase of certain assets in power systems. To mitigate this gap, a deep noisy filtering diagnostic model is proposed for accurate and rapid evaluations of power transformer faults using noisy vibration signals. A balanced isolation forest method is employed to detect abnormal data from the original vibration signals. Two deep noisy filter networks suppress the level of noise, based on which contrastive learning obtains the transformer fault states. Datasets collected from a 10-kV real power transformer validate the proposed model. The results demonstrate that the proposed method acquires a higher fault diagnostic accuracy with respect to the compared algorithms, showing the superiority and efficacy of the proposed model.
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